Main¶

=============== <Original Dataset> ===============
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20640 entries, 0 to 20639
Data columns (total 10 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   longitude           20640 non-null  float64
 1   latitude            20640 non-null  float64
 2   housing_median_age  20640 non-null  float64
 3   total_rooms         20640 non-null  float64
 4   total_bedrooms      20433 non-null  float64
 5   population          20640 non-null  float64
 6   households          20640 non-null  float64
 7   median_income       20640 non-null  float64
 8   median_house_value  20640 non-null  float64
 9   ocean_proximity     20640 non-null  object 
dtypes: float64(9), object(1)
memory usage: 1.6+ MB
None

longitude latitude housing_median_age total_rooms total_bedrooms population households median_income median_house_value ocean_proximity
0 -122.23 37.88 41.0 880.0 129.0 322.0 126.0 8.3252 452600.0 NEAR BAY
1 -122.22 37.86 21.0 7099.0 1106.0 2401.0 1138.0 8.3014 358500.0 NEAR BAY
2 -122.24 37.85 52.0 1467.0 190.0 496.0 177.0 7.2574 352100.0 NEAR BAY
3 -122.25 37.85 52.0 1274.0 235.0 558.0 219.0 5.6431 341300.0 NEAR BAY
4 -122.25 37.85 52.0 1627.0 280.0 565.0 259.0 3.8462 342200.0 NEAR BAY
... ... ... ... ... ... ... ... ... ... ...
20635 -121.09 39.48 25.0 1665.0 374.0 845.0 330.0 1.5603 78100.0 INLAND
20636 -121.21 39.49 18.0 697.0 150.0 356.0 114.0 2.5568 77100.0 INLAND
20637 -121.22 39.43 17.0 2254.0 485.0 1007.0 433.0 1.7000 92300.0 INLAND
20638 -121.32 39.43 18.0 1860.0 409.0 741.0 349.0 1.8672 84700.0 INLAND
20639 -121.24 39.37 16.0 2785.0 616.0 1387.0 530.0 2.3886 89400.0 INLAND

20640 rows × 10 columns

=============== <Modified Dataset> ===============
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20433 entries, 0 to 20432
Data columns (total 9 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   longitude           20433 non-null  float64
 1   latitude            20433 non-null  float64
 2   housing_median_age  20433 non-null  float64
 3   total_rooms         20433 non-null  float64
 4   total_bedrooms      20433 non-null  float64
 5   population          20433 non-null  float64
 6   households          20433 non-null  float64
 7   median_income       20433 non-null  float64
 8   ocean_proximity     20433 non-null  object 
dtypes: float64(8), object(1)
memory usage: 1.4+ MB
None

longitude latitude housing_median_age total_rooms total_bedrooms population households median_income ocean_proximity
0 -122.23 37.88 41.0 880.0 129.0 322.0 126.0 8.3252 NEAR BAY
1 -122.22 37.86 21.0 7099.0 1106.0 2401.0 1138.0 8.3014 NEAR BAY
2 -122.24 37.85 52.0 1467.0 190.0 496.0 177.0 7.2574 NEAR BAY
3 -122.25 37.85 52.0 1274.0 235.0 558.0 219.0 5.6431 NEAR BAY
4 -122.25 37.85 52.0 1627.0 280.0 565.0 259.0 3.8462 NEAR BAY
... ... ... ... ... ... ... ... ... ...
20428 -121.09 39.48 25.0 1665.0 374.0 845.0 330.0 1.5603 INLAND
20429 -121.21 39.49 18.0 697.0 150.0 356.0 114.0 2.5568 INLAND
20430 -121.22 39.43 17.0 2254.0 485.0 1007.0 433.0 1.7000 INLAND
20431 -121.32 39.43 18.0 1860.0 409.0 741.0 349.0 1.8672 INLAND
20432 -121.24 39.37 16.0 2785.0 616.0 1387.0 530.0 2.3886 INLAND

20433 rows × 9 columns

=============== AutoML Start ===============
=============== Model : KMeans ===============
Start calculating silhouette_score...( method = KMeans )
Calculating silhouette_score ( k = 2 )
Calculating silhouette_score ( k = 3 )
Calculating silhouette_score ( k = 4 )
Calculating silhouette_score ( k = 5 )
Calculating silhouette_score ( k = 6 )
Calculating silhouette_score ( k = 7 )
Calculating silhouette_score ( k = 8 )
Calculating silhouette_score ( k = 9 )
Calculating silhouette_score ( k = 10 )
Calculating silhouette_score ( k = 11 )
Calculating silhouette_score ( k = 12 )
Calculating silhouette_score ( k = 2 )
Calculating silhouette_score ( k = 3 )
Calculating silhouette_score ( k = 4 )
Calculating silhouette_score ( k = 5 )
Calculating silhouette_score ( k = 6 )
Calculating silhouette_score ( k = 7 )
Calculating silhouette_score ( k = 8 )
Calculating silhouette_score ( k = 9 )
Calculating silhouette_score ( k = 10 )
Calculating silhouette_score ( k = 11 )
Calculating silhouette_score ( k = 12 )
best K_s = [2, 5]
max_iter = 100 / algorithm = full / k = 2 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0    18112
1.0     2321
Name: median_house_value, dtype: int64
===max===
predict
0.0    500001.0
1.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
0.0    179200.0
1.0    185400.0
Name: median_house_value, dtype: float64
===min===
predict
0.0    14999.0
1.0    22500.0
Name: median_house_value, dtype: float64
===mean===
predict
0.0    206719.410004
1.0    211908.015941
Name: median_house_value, dtype: float64
max_iter = 100 / algorithm = elkan / k = 2 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0    18112
1.0     2321
Name: median_house_value, dtype: int64
===max===
predict
0.0    500001.0
1.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
0.0    179200.0
1.0    185400.0
Name: median_house_value, dtype: float64
===min===
predict
0.0    14999.0
1.0    22500.0
Name: median_house_value, dtype: float64
===mean===
predict
0.0    206719.410004
1.0    211908.015941
Name: median_house_value, dtype: float64
max_iter = 300 / algorithm = full / k = 2 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0    18112
1.0     2321
Name: median_house_value, dtype: int64
===max===
predict
0.0    500001.0
1.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
0.0    179200.0
1.0    185400.0
Name: median_house_value, dtype: float64
===min===
predict
0.0    14999.0
1.0    22500.0
Name: median_house_value, dtype: float64
===mean===
predict
0.0    206719.410004
1.0    211908.015941
Name: median_house_value, dtype: float64
max_iter = 300 / algorithm = elkan / k = 2 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0    18112
1.0     2321
Name: median_house_value, dtype: int64
===max===
predict
0.0    500001.0
1.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
0.0    179200.0
1.0    185400.0
Name: median_house_value, dtype: float64
===min===
predict
0.0    14999.0
1.0    22500.0
Name: median_house_value, dtype: float64
===mean===
predict
0.0    206719.410004
1.0    211908.015941
Name: median_house_value, dtype: float64
max_iter = 500 / algorithm = full / k = 2 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0    18112
1.0     2321
Name: median_house_value, dtype: int64
===max===
predict
0.0    500001.0
1.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
0.0    179200.0
1.0    185400.0
Name: median_house_value, dtype: float64
===min===
predict
0.0    14999.0
1.0    22500.0
Name: median_house_value, dtype: float64
===mean===
predict
0.0    206719.410004
1.0    211908.015941
Name: median_house_value, dtype: float64
max_iter = 500 / algorithm = elkan / k = 2 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0    18112
1.0     2321
Name: median_house_value, dtype: int64
===max===
predict
0.0    500001.0
1.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
0.0    179200.0
1.0    185400.0
Name: median_house_value, dtype: float64
===min===
predict
0.0    14999.0
1.0    22500.0
Name: median_house_value, dtype: float64
===mean===
predict
0.0    206719.410004
1.0    211908.015941
Name: median_house_value, dtype: float64
max_iter = 100 / algorithm = full / k = 5 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0    1703
1.0    7544
2.0    8334
3.0     276
4.0    2576
Name: median_house_value, dtype: int64
===max===
predict
0.0    500001.0
1.0    500001.0
2.0    500001.0
3.0    500001.0
4.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
0.0    166300.0
1.0    175000.0
2.0    184200.0
3.0    180200.0
4.0    183800.0
Name: median_house_value, dtype: float64
===min===
predict
0.0    22500.0
1.0    14999.0
2.0    14999.0
3.0    47500.0
4.0    22500.0
Name: median_house_value, dtype: float64
===mean===
predict
0.0    197965.870229
1.0    200971.803552
2.0    214296.768778
3.0    207263.068841
4.0    209440.767857
Name: median_house_value, dtype: float64
max_iter = 100 / algorithm = elkan / k = 5 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0    1703
1.0    7544
2.0    8334
3.0     276
4.0    2576
Name: median_house_value, dtype: int64
===max===
predict
0.0    500001.0
1.0    500001.0
2.0    500001.0
3.0    500001.0
4.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
0.0    166300.0
1.0    175000.0
2.0    184200.0
3.0    180200.0
4.0    183800.0
Name: median_house_value, dtype: float64
===min===
predict
0.0    22500.0
1.0    14999.0
2.0    14999.0
3.0    47500.0
4.0    22500.0
Name: median_house_value, dtype: float64
===mean===
predict
0.0    197965.870229
1.0    200971.803552
2.0    214296.768778
3.0    207263.068841
4.0    209440.767857
Name: median_house_value, dtype: float64
max_iter = 300 / algorithm = full / k = 5 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0    1703
1.0    7544
2.0    8334
3.0     276
4.0    2576
Name: median_house_value, dtype: int64
===max===
predict
0.0    500001.0
1.0    500001.0
2.0    500001.0
3.0    500001.0
4.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
0.0    166300.0
1.0    175000.0
2.0    184200.0
3.0    180200.0
4.0    183800.0
Name: median_house_value, dtype: float64
===min===
predict
0.0    22500.0
1.0    14999.0
2.0    14999.0
3.0    47500.0
4.0    22500.0
Name: median_house_value, dtype: float64
===mean===
predict
0.0    197965.870229
1.0    200971.803552
2.0    214296.768778
3.0    207263.068841
4.0    209440.767857
Name: median_house_value, dtype: float64
max_iter = 300 / algorithm = elkan / k = 5 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0    1703
1.0    7544
2.0    8334
3.0     276
4.0    2576
Name: median_house_value, dtype: int64
===max===
predict
0.0    500001.0
1.0    500001.0
2.0    500001.0
3.0    500001.0
4.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
0.0    166300.0
1.0    175000.0
2.0    184200.0
3.0    180200.0
4.0    183800.0
Name: median_house_value, dtype: float64
===min===
predict
0.0    22500.0
1.0    14999.0
2.0    14999.0
3.0    47500.0
4.0    22500.0
Name: median_house_value, dtype: float64
===mean===
predict
0.0    197965.870229
1.0    200971.803552
2.0    214296.768778
3.0    207263.068841
4.0    209440.767857
Name: median_house_value, dtype: float64
max_iter = 500 / algorithm = full / k = 5 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0    1703
1.0    7544
2.0    8334
3.0     276
4.0    2576
Name: median_house_value, dtype: int64
===max===
predict
0.0    500001.0
1.0    500001.0
2.0    500001.0
3.0    500001.0
4.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
0.0    166300.0
1.0    175000.0
2.0    184200.0
3.0    180200.0
4.0    183800.0
Name: median_house_value, dtype: float64
===min===
predict
0.0    22500.0
1.0    14999.0
2.0    14999.0
3.0    47500.0
4.0    22500.0
Name: median_house_value, dtype: float64
===mean===
predict
0.0    197965.870229
1.0    200971.803552
2.0    214296.768778
3.0    207263.068841
4.0    209440.767857
Name: median_house_value, dtype: float64
max_iter = 500 / algorithm = elkan / k = 5 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0    1703
1.0    7544
2.0    8334
3.0     276
4.0    2576
Name: median_house_value, dtype: int64
===max===
predict
0.0    500001.0
1.0    500001.0
2.0    500001.0
3.0    500001.0
4.0    500001.0
Name: median_house_value, dtype: float64
===median===
predict
0.0    166300.0
1.0    175000.0
2.0    184200.0
3.0    180200.0
4.0    183800.0
Name: median_house_value, dtype: float64
===min===
predict
0.0    22500.0
1.0    14999.0
2.0    14999.0
3.0    47500.0
4.0    22500.0
Name: median_house_value, dtype: float64
===mean===
predict
0.0    197965.870229
1.0    200971.803552
2.0    214296.768778
3.0    207263.068841
4.0    209440.767857
Name: median_house_value, dtype: float64